Improvement of satellite data
This project aims to improve satellite (not limited to) data. The method that we are currently developing is merging multiple datasets derived from various sources
This project aims to improve satellite (not limited to) data. The method that we are currently developing is merging multiple datasets derived from various sources
This project aims to evaluate various satellite data for further improvement and/or applications.
This project aims to apply and analyze satellite (not limited to) data.
Published in Water Resources Research, 2022
Model calibration using satellite-derived streamflow.
Recommended citation: Yoon, H.N., Marshall, L., Sharma, A., Kim, S. (2022), Bayesian model calibration using surrogate streamflow in ungauged catchments, Water Resources Research, 58(1), e2021WR031287
MATLAB codes for optimizing model parameters and simulating floods (Kim et al.,2018)
MATLAB codes for dynamic linear combination of soil moisture datasets (Kim et al.,2016)
MATLAB codes for SNR-opt for merging datasets (Kim et al.,2021)
Published:
Kim S., Liu Y., Johnson F., Parinussa R., Sharma A. Improvement of Soil Moisture Dataset Combining AMSR2 Soil Moisture Products, The Australian Energy and Water Exchange Initiative (OzEWEX) 2014, Canberra, ACT, Australia Link Download
Published:
S. Kim, Y. Liu, F. Johnson, R. Parinussa, A. Sharma. Reducing Structural Uncertainty in AMSR2 Soil Moisture Using a Model Combination Approach, American Geophysical Union (AGU) fall meeting 2014, San Francisco, CA, USA Link Download
Published:
Kim S., Liu Y., Johnson F., Sharma A. A temporal correlation-based approach for spatial disaggregation of remotely sensed soil moisture, American Geophysical Union (AGU) fall meeting 2016, San Francisco, CA, USA Link Download
Published:
Kim S., Ajami H., Sharma A. Incorporating an operational satellite-derived leaf area index into a computationally efficient semi-distributed hydrologic modelling application (SMART), The 22nd International Congress on Modelling and Simulation (MODSIM2017), Hobart, Australia Link Download
Published:
Kim S., Guo Y., Wasko C., Sharma A. On soil moisture, rain and flood extremes in a warming climate - using satellite remote sensing to define future antecedent conditions, The Korean Society of Climate Change Research (KSCC) 2018, Jeju, Republic of Korea Link Download
Published:
Kim S., Pham H., Liu Y., Sharma A., Marshall L. Combining geophysical variables for maximizing temporal correlation without reference data, The 23rd International Congress on Modelling and Simulation (MODSIM2019), Canberra, Australia Link Download
Published:
Kim S., Zhang R., Sharma A., Lakshmi V. Improvements of satellite observations through data merging: status and challenges, AGU Fall Meeting 2020, Online Link Download
Published:
Kim S., Sharma A., Wasko C., Nathan R. How does total precipitable water link to precipitation extremes?, MODSIM 2021, Sydney, Australia Link
Masters course, School of Civil and Environmental Engineering, UNSW Sydney, 2017
Masters course, School of Civil and Environmental Engineering, UNSW Sydney, 2018
Undergraduate course, School of Civil and Environmental Engineering, UNSW Sydney, 2019
Masters course, School of Civil and Environmental Engineering, UNSW Sydney, 2019
Undergraduate course, School of Civil and Environmental Engineering, UNSW Sydney, 2020
Masters course, School of Civil and Environmental Engineering, UNSW Sydney, 2020